Data Preparation and Cleaning
Brian Stacy
2020-08-06
- Introduction
- Data Use
- Data Services
- Data Products
- AKI 3.1, 3.9, 3.10, 3.11, 3.12, 3.13
- AKI 3.1: Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)
- AKI 3.2: Hunger
- AKI 3.3: Mortality rate, under-5 (per 1,000 live births)
- AKI 3.5 Maternal Mortality Ratio
- AKI 3.7: Access to electricity (% of population)
- AKI 3.14 Quarterly GDP
- AKI 3.15 Debt service (PPG and IMF only, % of exports of goods, services and primary income)
- Data Sources
- Data Infrastructure
# This block of code will create a blank SPI dataset (only containing country info) that will be appended to when each indicator is added.
# There will be two indicators added for each dimension
# 1. An indicator with a score between 0-1 for each dimension
# 2. An indicator with the raw (unscored) values of the indicators
# The unit for this database will be country*year
span <- c(2004:2019)
spi_df_empty <- bind_rows(replicate(length(span), wbstats::wbcountries(), simplify = FALSE), .id='date') %>%
mutate(date=as.numeric(date)+span[1]-1) %>%
filter(region!="Aggregates") # take out the aggregates (LAC, SAR, etc)
spi_df <- spi_df_emptyIntroduction
In this program, I will clean and reshape the raw data files in the 01_rawdata folder in order to produce the Statistical Performance Indicators.
Data Use
Cleaning for Data Use Indicators. Data Use (5 Indicators):
- 1.1_DUNL - Indicator 1.1: Data use by national legislature
- 1.2_DUNE - Indicator 1.2: Data use by national executive branch
- 1.3_DUCS - Indicator 1.3: Data use by civil society
- 1.4_DUAC - Indicator 1.4: Data use by academia
- 1.5_DUIO - Indicator 1.5: Data use by international organizations
Indicator 1.1: Data use by national legislature
Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using national legislature website as source.
Indicator 1.2: Data use by national executive branch
Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using national development plans and poverty reduction plans as a source.
Indicator 1.3: Data use by civil society
Based on PARIS21 data use indicator (Chapter 4 of Statistical Capacity Development Outlook) using main social media platform in use in country as source.
Indicator 1.4: Data use by academia
The idea for this indicator is that countries should be producing statistical products that are utilized, in this case by academia. As a measure of this, we use the IHSN database on censuses and surveys conducted by counties and calculate a ranking of countries based on the total number of downloads of the censuses and surveys produced by that country on a per capita basis. In other words, we calculate the total number of downloads of censuses and surveys for a country, divided by the population of that country, to produce a ranking.
The source for this indicator is the IHSN.
The mission of the IHSN is to improve the availability, accessibility, and quality of survey data within developing countries, and to encourage the analysis and use of this data by national and international development decision makers, the research community, and other stakeholders.
To support this mission, the key objectives of the IHSN are:
Improved coordination of internationally sponsored survey programs, with emphasis on timing, sequencing, frequency, and cost-effectiveness Availability of coordinated and practical technical and methodological guidelines for all stages of the survey life cycle Availability of a central survey data catalog which would inform data users of the availability of survey and census data from multiple sources Availability of standards, tools, and guidelines that would allow data producers to document, disseminate, and preserve microdata according to international standards and best practices Improved collaboration between data producers and users
IHSN has a database of more than 7,483 surveys as of August 5, 2020.
#####
# The following code is blocked out, because the execution time is long
#####
# # pull all surveys from IHSN
# ihsn_base_url <- "https://catalog.ihsn.org/index.php/api/catalog" #define url
# study_request_ihsn<-fromJSON(paste(ihsn_base_url,"/search","?ps=10000", sep="")) # pull from url
# study_df_ihsn <- study_request_ihsn$result$rows #convert to dataframe
#
# #get list of survey ids
# ihsn_series <- study_df_ihsn$idno
#
#
#
# #use purr to loop over list of surveys and return some extra info
#
# series_info <- function(series) {
#
# info <- fromJSON(paste('https://catalog.ihsn.org/index.php/api/catalog/',series, sep=""))
# info$dataset$metadata$study_desc$series_statement$series_name
#
# }
#
# #now produce a dataframe with the more info on survey
# study_df_ihsn <- study_df_ihsn %>%
# mutate(series_info=map(ihsn_series, possibly(series_info,
# otherwise = 'Something Wrong'
# )
# )
# )
#
# study_df_ihsn <- study_df_ihsn %>%
# mutate(across(is.list, as.character))
#
# write_excel_csv(study_df_ihsn, paste(raw_dir, '1.4_DUAC', 'ihsn_citations.csv', sep="/"))
# pull in dataset with citation counts
study_df_ihsn <- read_csv(paste(raw_dir, '1.4_DUAC', 'ihsn_citations.csv', sep="/"))
#categories surveys in IHSN
study_df_ihsn <- study_df_ihsn %>%
mutate(topic=case_when(
grepl('Population|Housing|Censuses',series_info) ~ 'Population and Housing Censuses',
grepl('lfs',series_info) ~ 'Labor Force Surveys',
grepl('dhs|DHS|mics|MICS|whs|hea',series_info) ~ 'Health Surveys',
grepl('ies',series_info) ~ 'Income/Expenditure Surveys',
grepl('sems',series_info) ~ 'Socio/Economic Monitoring Surveys',
grepl('lsms', series_info) ~ 'Living Standards Measurement Study',
grepl('ag', series_info) ~ 'Agriculture Survey or Census',
grepl('en', series_info) ~ 'Enterprise Survey',
grepl('oth',series_info) ~ 'Other Household Surveys',
grepl('Opinion', series_info) ~ 'Opinion Survey',
TRUE ~ 'Other'
)) %>%
mutate(date=year_end,
country=nation)
##########
#Form measures of academic use
##########
#get population data to produce per-capita measure
pop <-wbstats::wb(country="countries_only",
indicator='SP.POP.TOTL',
startdate=2004,
enddate=2019,
removeNA=FALSE) %>%
transmute(
iso3c=iso3c,
country=country,
date=as.numeric(date),
population=value
)
#get topics list
topics_list <- unique(study_df_ihsn$topic)
#Now calculate our SPI score for this indicator
for (i in 2004:2019) {
temp <- study_df_ihsn %>%
filter(date<=i) %>% #get a list of all surveys conducted before the year
group_by(country, topic) %>%
summarise(total_downloads=sum(total_downloads))
#add grand total
temp_tot <- study_df_ihsn %>%
filter(date<=i) %>% #get a list of all surveys conducted before the year
group_by(country) %>%
summarise(total_downloads=sum(total_downloads)) %>%
mutate(topic="Total")
temp <- temp %>%
bind_rows(temp_tot) %>%
mutate(date=i) %>%
pivot_wider(names_from='topic',
values_from='total_downloads') %>%
mutate(across(topics_list, ~if_else(is.na(.),0,.))) %>%
arrange(date, country)
assign(paste('academic_use_df',i,sep="_"), temp)
}
academic_use_df <- academic_use_df_2019
for (i in 2004:2018) {
academic_use_df <- bind_rows(academic_use_df, get(paste('academic_use_df',i,sep="_")))
}
academic_use_df <- academic_use_df %>%
left_join(pop) %>%
right_join(spi_df_empty) %>%
filter(!is.na(income)) %>%
mutate(downloads_per_capita=1000*Total/population) %>% #create measure of downloads per 1,000,000 persons
mutate(downloads_per_capita=if_else(is.na(downloads_per_capita),0,downloads_per_capita)) %>%
group_by(date) %>%
mutate(academic_use_rank = rank(-downloads_per_capita))
academic_use_map <- academic_use_df %>%
filter(downloads_per_capita>0)
Indicator 1.5: Data use by international organizations
New indicator of level of congruence between UNSD and national database of SDG indicators. For each of the indicators calculate whether the number in the database for the most recent year is the same or different. The indicator is the percentage that are the same.
Data Services
Cleaning for Data Services Indicators. Data Services (4 Indicators):
- 2.1_DSDR - Indicator 2.1: Data releases
- 2.2_DSOA - Indicator 2.2: Online access
- 2.3_DSAS - Indicator 2.3: Advisory/ Analytical Services
- 2.4_DSDS - Indicator 2.4: Data services
Indicator 2.1: Data Releases
Data Dissemination Standard (SDDS) and electronic General Data Dissemination Standard (e-GDDS) were established by the International Monetary Fund (IMF) for member countries that have or that might seek access to international capital markets, to guide them in providing their economic and financial data to the public. Although subscription is voluntary, the subscribing member needs to be committed to observing the standard and provide information about its data and data dissemination practices (metadata). The metadata are posted on the IMF’s SDDS and e-GDDS websites.
1 Point. Subscribing to IMF SDDS+ or SDDS standards 0.5 Points. Subscribing to IMF e-GDDS standards 0 Points. Otherwise
Request_metadata <- GET(url = "http://api.worldbank.org/v2/country/all/indicator/5.21.01.01.sdds?format=json&date=2004:2015&per_page=5000")
Response_metadata <- content(Request_metadata, as = "text", encoding = "UTF-8")
# Parse the JSON content and convert it to a data frame.
D2.2_DSDR_sci <- jsonlite::fromJSON(Response_metadata, flatten = TRUE) %>%
data.frame() %>%
transmute(
iso3c=countryiso3code,
country=country.value,
date=as.numeric(date),
SDDS=value
) %>%
left_join(spi_df_empty) %>% #add on country metadata
filter(!is.na(income)) %>%
select(iso3c, country, date, SDDS )
#read in csv file.
D2.2_DSDR <- read_csv(file = paste(raw_dir, '/2.1_DSDR/', "D2.1.GDDS.csv", sep="" )) %>%
mutate(D2.1.GDDS=case_when(
IDDS=="SDDS Plus" | IDDS=="SDDS"~ 1,
IDDS=="e-GDDS"~ 0.5,
TRUE ~ 0 ),
SDDS=case_when(
IDDS=="SDDS"~ 1,
TRUE ~ 0 )) %>%
bind_rows(D2.2_DSDR_sci) %>%
select(iso3c, country, date, IDDS,SDDS, D2.1.GDDS ) %>%
arrange(date, country)
D2.2_DSDR_map <- D2.2_DSDR %>%
filter(SDDS>0)
spi_mapper('D2.2_DSDR_map', 'SDDS', 'SDDS Data Standards in Place' )
#add to spi databases
spi_df <- spi_df %>%
left_join(D2.2_DSDR)Indicator 2.2: Online access
This indicator measures the richness and openness of online access.
Source
Our source for this indicator is Open Data Watch. From Open Data watch:
The Open Data Inventory (ODIN) assesses the coverage and openness of official statistics to help identify gaps, promote open data policies, improve access, and encourage dialogue between national statistical offices (NSOs) and data users. ODIN 2018/19 includes 178 countries, including most all OECD countries. Two-year comparisons are for all countries with two years of data between 2015-2017. Scores can be compared across topics and countries.
We use the Openness score from ODIN for this measure. The score ranges from 0-100. It contains scores along five dimensions:
- Machine Readability
- Non-Proprietary format
- Download Options
- Metadata Available
- Terms of Use
A description for each of these five dimensions is below:
Machine Readability
Openness element 1 measures whether data are available in a machine readable format such as XLS, XLSX, CSV, and JSON. Machine-readable file formats allow users to easily process data using a computer. When data are made available in formats that are not machine readable, users cannot easily access and modify the data, which severely restricts the scope of the data’s use. In many cases PDF versions of datasets within reports can be useful to users, as the text in conjunction with the tables gives context and explanation to the figures which helps less technical users understand the data. Because of this, ODIN assessments do not penalize countries for making datasets available in PDF or other non-machine readable formats, unless these formats are the only option for exporting data. Scores are not penalized for having identical datasets in both machine readable and non-readable formats. Compression formats do not affect machine readability scores, only non-proprietary scores (see next page). Scores are given by data category, not indicator.
Non-Proprietary format
For the elements of data openness, scoring is calculated independent of the data coverage. If data files are compressed in RAR format (which is proprietary), data for that indicator should be considered proprietary even if the enclosing files are in a non-proprietary format. Files compressed in ZIP format are not affected.
Download Options
Openness element 3 measures whether data are available with three different download options: bulk download, API, and user-select options. A bulk download is defined at the indicator level as: The ability to download all data recorded in ODIN for a particular indicator (all years, disaggregations, and subnational data) in one file, or multiple files that can be downloaded simultaneously. Bulk downloads are a key component of the Open Definition, which requires data to be “provided as a whole . . . and downloadable via the internet.” User-selectable download options are defined as: Users must be able to select an indicator and at least one other dimension to create a download or table. These dimensions could include time periods, geographic disaggregations, or other recommended disaggregations. An option to choose the file export format is not enough. API stands for Application Programming Interface. Ideally, APIs should be clearly displayed on the website. ODIN assumes APIs are available for the NSOs entire data collection used in ODIN, unless clearly stated. ODIN assessors do not register for use or test API functionality. For more information on APIs, see this guide. Scores are given by data category, not indicator.
Metadata Available
Openness element 4 measures whether metadata are made available. Scores are given by data category, not indicator. Metadata are defined at the indicator level as information about how the data are defined/calculated and collected. ODIN classifies metadata into three categories: (1) Not Available, (2) Incomplete, and (3) Complete. The following must be available to classify metadata as complete: • Definition of the indicator, or definition of key terms used in the indicator description (as applicable), or how the indicator was calculated. • Publication (date of upload), compilation date (date on front of report is not sufficient), or date dataset was last updated. • Name of data source (what agency collected the data). If the metadata only have one or two of the above elements, they are scored as incomplete
Terms of Use
Openness element 5 measures whether data are available with an open terms of use. Generally, terms of use (TOU) will apply to an entire website or data portal (unless otherwise specified). In these cases, all data found on the same website and/or portal will receive the same score. If a portal is located on the same domain as the NSO website, the terms of use on the NSO site will apply. If the data are located on a portal or website on a different domain, another terms of use will need to be present. For a policy/ license to be accepted as a terms of use, it must clearly refer to the data found on the website. Terms of use that refer to nondata content (such as pictures, logos, etc.) of the website are not considered. A copyright symbol at the bottom of the page is not sufficient. A sentence indicating a recommended citation format is not sufficient. Terms of use are classified the following ways: (1) Not Available, (2) Restrictive, (3) Semi-Restrictive, and (4) Open. If the TOU contains one or more restrictive clauses, it receives 0 points and is classified as “restrictive.” Restrictive clauses include:
For more details, consult the ODIN technical documentation: https://docs.google.com/document/d/1ubPL1l_3im9bjlCVZ6W2ICAy6UAiXl1hGeA1aXImkxI/edit#
#read in ODIN data
for (i in 2015:2018) {
temp <- read_csv(paste(raw_dir, '/2.2_DSOA/','ODIN_',i,'.csv', sep="")) %>%
as_tibble(.name_repair = 'universal') %>%
mutate(date=i) %>%
filter(Data.categories=='All Categories')
assign(paste('openness_df',i,sep="_"), temp)
}
#bind different years together
openness_df <- bind_rows(openness_df_2015, openness_df_2016, openness_df_2017, openness_df_2018)
openness_df <- openness_df %>%
select(Country.Code, date, Machine.readable, Non.proprietary, Download.options, Metadata.available, Terms.of.use, Openness.subscore) %>%
rename(iso3c=Country.Code) %>%
group_by( iso3c) %>%
right_join(country_metadata) %>%
mutate(
across(c('Machine.readable', 'Non.proprietary', 'Download.options', 'Metadata.available', 'Terms.of.use', 'Openness.subscore'),~if_else(is.na(.), 0, .) )
)
#add to spi dataframe
spi_df <- spi_df %>%
left_join(openness_df)
Indicator 2.3: Advisory/ Analytical Services
No established source exists for this indicator. This is experimental.
Indicator 2.4: Data services
NSO has a listing of surveys and microdata sets that can provide the necessary data and reference for follow-up. Upon well-defined request and procedure per the national law and practice, users and practitioners can obtain the data collected from the households and businesses when needed.
NADA is an open source microdata cataloging system, compliant with the Data Documentation Initiative (DDI) and Dublin Core’s RDF metadata standards. It serves as a portal for researchers to browse, search, compare, apply for access, and download relevant census or survey datasets, questionnaires, reports and other information.
1 Point. Yes 0 Points. No
#read in csv file.
D2.4_NADA <- read_csv(file = paste(raw_dir, '/2.4_DSDS/', "D2.4.NADA.csv", sep="/" )) %>%
mutate(D4.2.NADA=case_when(
NADA==1 ~ 1,
NADA==0 ~ 0,
TRUE ~ 0
)) %>%
select(iso3c, country, date, NADA,NADA_text, D4.2.NADA ) %>%
arrange(date, country)
D2.4_NADA_map <- D2.4_NADA %>%
filter(D4.2.NADA>0)
spi_mapper('D2.4_NADA_map', 'D4.2.NADA', 'NADA in Place' )
#add to spi databases
spi_df <- spi_df %>%
left_join(D2.4_NADA)Data Products
Cleaning for Data Products Indicators. Data Products (4 Indicators):
- 3.1_DPSS - Indicator 3.1: social statistics
- 3.2_DPES - Indicator 3.2: economic statistics
- 3.3_DPEN - Indicator 3.3 environmental statistics
- 3.4_DPIS - Indicator 3.4: institutional statistics
Below is the list of AKI indicators:
Indicator 3.1: social statistics
- AKI 3.1: Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)
- AKI 3.2: Food Insecurity Experience Scale - AKI 3.3: Mortality rate, under-5 (per 1,000 live births)
- AKI 3.4: Proportion of children and young people in grades 2 or 3 achieving at least a minimum proficiency level in reading and mathematics, by sex.
- AKI 3.5: Maternal Mortality - AKI 3.6: People using safely managed drinking water services (% of population)
Indicator 3.2: economic statistics - AKI 3.7: Access to electricity (% of population)
- AKI 3.8: Unemployment, total (% of total labor force)
- AKI 3.9: Manufacturing, value added (% of GDP)
- AKI 3.10: Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)
- AKI 3.14: Quarterly GDP
Indicator 3.3 environmental statistics
- AKI 3.11: Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
- AKI 3.12: Renewable energy consumption (% of total final energy consumption)
- AKI 3.13: Households and NPISHs Final consumption expenditure (current LCU)
Indicator 3.4: institutional statistics - AKI 3.15: Debt service (PPG and IMF only, % of exports of goods, services and primary income)
# pull in some WDI metadata which will be used for constructing AKI indicators.
# WDI metadata was gathered using previous vintages of WDI from 2016-19
# Metadata was gathered and saved as csv files in the 011_data folder
for (i in 2016:2019) {
temp <- read_csv(file = paste(raw_dir, "/metadata/WDI_metadata_",i,".csv", sep="" )) %>%
as_tibble(.name_repair='universal') %>%
mutate(National.accounts.reference.year=as.numeric(National.accounts.reference.year),
date=i)
assign(paste("WDI_metadata",i,sep="_"), temp)
}
WDI_metadata <- bind_rows(WDI_metadata_2016, WDI_metadata_2017, WDI_metadata_2018, WDI_metadata_2019)AKI 3.1, 3.9, 3.10, 3.11, 3.12, 3.13
First we will pull data for indicators coming straight from WDI. For some indicators, we will use alternative sources.
Indicators coming from the WDI directly are:
- AKI 3.1: Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)
- AKI 3.9: Manufacturing, value added (% of GDP)
- AKI 3.10: Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)
- AKI 3.11: Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
- AKI 3.12: Renewable energy consumption (% of total final energy consumption)
- AKI 3.13: Households and NPISHs Final consumption expenditure (current LCU)
Scoring:
1 Point. 3 or more values available within past 5 years 0.6 Points. 2 values available within past 5 years;
0.3 Points. 1 values available within past 5 years;
0 Points. None within past 5 years
##########
# Pull Tags for WDI Data
##########
# make request to World Bank API
wdiRequest <- GET(url = "http://api.worldbank.org/v2/indicator?per_page=20000&format=json&source=2")
wdiResponse <- content(wdiRequest, as = "text", encoding = "UTF-8")
# Parse the JSON content and convert it to a data frame.
wdisJSON <- jsonlite::fromJSON(wdiResponse, flatten = TRUE) %>%
data.frame()
EdStatsRequest <- GET(url = "http://api.worldbank.org/v2/indicator?per_page=20000&format=json&source=12")
EdStatsResponse <- content(EdStatsRequest, as = "text", encoding = "UTF-8")
# Parse the JSON content and convert it to a data frame.
EdStatsJSON <- jsonlite::fromJSON(EdStatsResponse, flatten = TRUE) %>%
data.frame()
aki<- c(
'Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold',
'People using safely managed drinking water services (% of population)',
'Unemployment, total (% of total labor force) (national estimate)' ,
'Manufacturing, value added (% of GDP)',
'Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)',
'Level of water stress: freshwater withdrawal as a proportion of available freshwater resources',
'Renewable energy consumption (% of total final energy consumption)',
'Households and NPISHs Final consumption expenditure (current LCU)',
'Debt service (PPG and IMF only, % of exports of goods, services and primary income)' )
get_tag_aki_df<-wdisJSON %>%
bind_rows(EdStatsJSON) %>%
filter((name %in% aki )) %>%
arrange(factor(name, levels = aki)) %>%
select(id, name, sourceOrganization)
#get WDI metadata infor
cache_list<-wbstats::wbcache()
country_list <- wbstats::wbcountries()
aki_list<-get_tag_aki_df[,'id']
for (reference_year in 2016:2019) {
temp <-wbstats::wb(country="countries_only",
indicator=aki_list,
startdate=reference_year-5,
enddate=reference_year,
return_wide = T,
removeNA=FALSE) %>%
filter(((reference_year-as.numeric(date))<=5) & (reference_year>=as.numeric(date))) %>% #filter out years outside reference window of 3 years
write_excel_csv(path = paste(output_dir, "/D3.AKI_data_pull_",reference_year,".csv", sep="" )) %>%
mutate_at(.vars=aki_list, ~if_else(is.na(.),0,1)) %>% #create 0,1 variable for whether data point exists for country
group_by(iso3c, country) %>%
summarise_all((~(if(is.numeric(.)) sum(., na.rm = TRUE) else first(.)))) %>% #group by country to create one observation per country containing whether or not data point existed
mutate_at(.vars=aki_list, ~case_when(
.>=3 ~ 1,
.==2 ~ 0.6,
.==1 ~ 0.3,
.==0 ~ 0,
TRUE ~ 0
)) %>% # 1 point for at least 3 values, 0.6 for 2 values, 0.3 for 1 values, 0 otherwise
mutate(date=reference_year) %>%
ungroup() %>%
select(iso3c, country, date, aki_list) %>%
rename_at(aki_list, ~(paste('SPI.D3.',.,sep=""))) %>% #add 'D3.' as prefix before these indicators.
left_join(country_list) #attach country metadata
assign(paste("D3.AKI",reference_year,sep="_"), temp)
}AKI 3.1: Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)
The data will be pulled from the WDI and combined with metadata from the Povcalnet
Scoring is as follows: Quality (0.5 points total): 0.5 Point. Comparable data lasting at least two years within past 5 years
0 Points. No comparable data within past 5 years
Frequency (0.5 points total): 0.5 Point. 3 or more values available within past 5 years
0.3 Points. 2 values available within past 5 years;
0.15 Points. 1 values available within past 5 years;
0 Points. None within past 5 years
library(povcalnetR)
#the code below is based on public released code by povcalnet team:
#https://github.com/worldbank/Global_Poverty_Blogs/blob/master/bg_povcalnet_comparability/R/comparability_breaks.R
# some contants
year_range <- 1990:2020
metadata_path <- "https://development-data-hub-s3-public.s3.amazonaws.com/ddhfiles/506801/povcalnet_comparability.csv"
# Load data ---------------------------------------------------------------
metadata <- read_csv(metadata_path)
cov_lkup <- c(3, 2, 1, 4)
names(cov_lkup) <- c("N", "U", "R", "A")
dat_lkup <- c(2,1)
names(dat_lkup) <- c("income","consumption")
pcn <- povcalnet()
pcn$coveragetype <- cov_lkup[pcn$coveragetype]
pcn$datatype <- dat_lkup[pcn$datatype]
df <- pcn %>%
inner_join(metadata, by = c("countrycode", "year", "coveragetype", "datatype"))
#Now loop from 2016 and 2019, keeping just data inside last 5 years.
for (reference_year in 2016:2019) {
temp1<-df %>%
filter(coveragetype %in% c(3,4)) %>% #keep just nationally representative samples
mutate(frequency=((reference_year-as.numeric(year))<=5) & (reference_year>=as.numeric(year))) %>%
filter(frequency==TRUE) %>%
group_by(countrycode, comparability) %>% #for each country and comparability type, get number of comparable estimates
summarise(SPI.QUAL.D3.POV=n()) %>%
ungroup() %>%
group_by(countrycode) %>% #now get a total by country with the max number of comparable estimates
summarise(SPI.QUAL.D3.POV=max(SPI.QUAL.D3.POV, na.rm=T)) %>%
mutate(SPI.QUAL.D3.POV=if_else(SPI.QUAL.D3.POV>=2,0.5,0)) #only give point if there is at least two observations that are comparable
temp <-df %>%
filter(coveragetype %in% c(3,4)) %>% #keep just nationally representative samples
mutate(frequency=((reference_year-as.numeric(year))<=5) & (reference_year>=as.numeric(year))) %>%
mutate(SPI.FREQ.D3.POV=if_else(frequency==TRUE,1,0)) %>% #create 0,1 variable for whether data point exists for country
group_by(countryname,countrycode) %>%
summarise(SPI.FREQ.D3.POV=sum(SPI.FREQ.D3.POV, na.rm=T)) %>%
mutate(SPI.FREQ.D3.POV=case_when(
SPI.FREQ.D3.POV>=3 ~ 0.5,
SPI.FREQ.D3.POV==2 ~ 0.3,
SPI.FREQ.D3.POV==1 ~ 0.15,
SPI.FREQ.D3.POV==0 ~ 0,
TRUE ~ 0
)) %>%
left_join(temp1) %>% #now bring in quality dimension
mutate(SPI.QUAL.D3.POV=if_else(!is.na(SPI.QUAL.D3.POV),SPI.QUAL.D3.POV,0)) %>% #recode some missing values as zero, who had no observations in 5 year window
mutate(SPI.D3.POV=SPI.FREQ.D3.POV+SPI.QUAL.D3.POV) %>%
mutate(date=reference_year,
country=countryname,
iso3c=countrycode) %>%
select( country,iso3c, date, starts_with('SPI.D3.POV'))
assign(paste("D3.1.AKI",reference_year,sep="_"), temp)
}
D3.1.AKI <- bind_rows(D3.1.AKI_2016, D3.1.AKI_2017, D3.1.AKI_2018, D3.1.AKI_2019)AKI 3.2: Hunger
We examine the Food Insecurity Experience Scale from the FAO (http://www.fao.org/faostat/en/#data/FS). Data for food insecurity was pulled on May 1, 2020.
Scoring 1 Point. 3 or more values available within past 5 years 0.6 Points. 2 values available within past 5 years;
0.3 Points. 1 values available within past 5 years;
0 Points. None within past 5 years
#Read in data from UN Inter-agency Group for Child Mortality Estimation
D3.2.AKI.FIES <- read_csv(file=paste(raw_dir, '3.1_DPSS/D3.2.FIES.csv', sep="/")) %>%
rename(year_code="Year Code",
iso3c="ISO3 Code") %>%
mutate(date=as.numeric(str_extract(year_code, "^.{4}")),
SPI.D3.FIES=Value)
#Now loop from 2016 and 2019, keeping just data inside last 5 years.
for (reference_year in 2016:2019) {
temp <-D3.2.AKI.FIES %>%
filter(((reference_year-as.numeric(date))<=5) & (reference_year>=as.numeric(date))) %>% #filter out years outside reference window of 5 years
mutate(SPI.D3.FIES=if_else(is.na(SPI.D3.FIES),0,1)) %>% #create 0,1 variable for whether data point exists for country
group_by(iso3c) %>%
summarise_all((~(if(is.numeric(.)) sum(., na.rm = TRUE) else first(.)))) %>% #group by country to create one observation per country containing whether or not data point existed
mutate(SPI.D3.FIES=case_when(
SPI.D3.FIES>=3 ~ 1,
SPI.D3.FIES==2 ~ 0.6,
SPI.D3.FIES==1 ~ 0.3,
SPI.D3.FIES==0 ~ 0,
TRUE ~ 0
)) %>% # 1 point for at least 3 values, 0.6 for 2 values, 0.3 for 1 values, 0 otherwise
mutate(date=reference_year) %>%
select( iso3c, date, starts_with('SPI.'))
assign(paste("D3.2.AKI",reference_year,sep="_"), temp)
}
D3.2.AKI <- bind_rows(D3.2.AKI_2016, D3.2.AKI_2017, D3.2.AKI_2018, D3.2.AKI_2019)AKI 3.3: Mortality rate, under-5 (per 1,000 live births)
The data in WDI is modeled, based on HHS and Vital Registration. We use the following as a source for raw data produced by national statistical offices that is used in this modeling.
https://childmortality.org/data
Data was pulled on April 13, 2020.
Countries are ranked by source quality (admin data > survey > no data) and frequency. Score on this indicator has a max of 1 point with 0.5 points for source quality and 0.5 points for frequency. Detailed scoring for source quality and frequency components are below.
Quality (0.5 points total): 0.5 Point. Vital Registration data available within past 5 years
0.25 Points. Survey Data availabe, but no Vital Registration data within past 5 years;
0 Points. None within past 5 years
Frequency (0.5 points total): 0.5 Point. 3 or more values available within past 5 years
0.3 Points. 2 values available within past 5 years;
0.15 Points. 1 values available within past 5 years;
0 Points. None within past 5 years
#Read in data from UN Inter-agency Group for Child Mortality Estimation
D3.3.AKI.MORT <- read_csv(file=paste(raw_dir, '3.1_DPSS/D3.3_child_mort_RAW_v01.csv', sep="/")) %>%
filter(INDICATOR=='Under-five mortality rate' & SEX=='Total') %>% #keep just observations for under 5 child mortality and for both sexes
filter(OBS_STATUS=='Included in IGME') %>% # Also keep only surveys that met IGME criteria for inclusion as a nationally representative statistic
mutate(date=as.numeric(str_extract(TIME_PERIOD, "^.{4}")),
country=REF_AREA,
D3.CHLD.MORT=OBS_VALUE)
#Now loop from 2016 and 2019, keeping just data inside last 5 years.
for (reference_year in 2016:2019) {
temp <-D3.3.AKI.MORT %>%
mutate(frequency=((reference_year-as.numeric(date))<=5) & (reference_year>=as.numeric(date))) %>%
mutate(SPI.FREQ.D3.CHLD.MORT=if_else(frequency==TRUE,1,0)) %>% #create 0,1 variable for whether data point exists for country
mutate(SPI.QUAL.D3.CHLD.MORT=case_when(
reference_year-as.numeric(date)>=5 ~ 0, #outside 5 year window
SERIES_METHOD=='Vital Registration' ~ 0.5,
SERIES_METHOD!='Vital Registration' ~ 0.25,
TRUE ~ as.numeric(NA)
)) %>%
group_by(country) %>%
summarise(SPI.FREQ.D3.CHLD.MORT=sum(SPI.FREQ.D3.CHLD.MORT, na.rm=T),
SPI.QUAL.D3.CHLD.MORT=max(SPI.QUAL.D3.CHLD.MORT, na.rm=T)) %>%
mutate(SPI.FREQ.D3.CHLD.MORT=case_when(
SPI.FREQ.D3.CHLD.MORT>=3 ~ 0.5,
SPI.FREQ.D3.CHLD.MORT==2 ~ 0.3,
SPI.FREQ.D3.CHLD.MORT==1 ~ 0.15,
SPI.FREQ.D3.CHLD.MORT==0 ~ 0,
TRUE ~ 0
)) %>%
mutate(SPI.D3.CHLD.MORT=SPI.FREQ.D3.CHLD.MORT+SPI.QUAL.D3.CHLD.MORT) %>%
mutate(date=reference_year) %>%
select( country, date, starts_with('SPI.'))
assign(paste("D3.3.AKI",reference_year,sep="_"), temp)
}
D3.3.AKI <- bind_rows(D3.3.AKI_2016, D3.3.AKI_2017, D3.3.AKI_2018, D3.3.AKI_2019)AKI 3.5 Maternal Mortality Ratio
The data is sourced from the the Inter-Agency Group (200 countries) to improve time and country coverages. Tne national estimates with the source information are available at https://www.who.int/reproductivehealth/publications/maternal-mortality-2000-2017/en/. The data was accessed on April 3, 2020. This data was used for modelling maternal mortality, which are then fed into the modelled estimates on Maternal Mortality (SH.STA.MMRT) in the WDI.
For our purposes, we will use the raw data from the country surveys as our data for the availability of key indicators, since the modelled estimates are produced out of sample, and thus not reflective of a country’s national statistical system.
Countries are ranked by source quality (admin data > survey > no data) and frequency. Score on this indicator has a max of 1 point with 0.5 points for source quality and 0.5 points for frequency. Detailed scoring for source quality and frequency components are below.
Quality (0.5 points total): 0.5 Point. Vital Registration data available within past 5 years
0.25 Points. Survey Data or Census Data availabe, but no Vital Registration data within past 5 years;
0 Points. None within past 5 years
Frequency (0.5 points total): 0.5 Point. 3 or more values available within past 5 years
0.3 Points. 2 values available within past 5 years;
0.15 Points. 1 values available within past 5 years;
0 Points. None within past 5 years
#Read in data from UN Inter-agency Group
D3.5.AKI.MMR <- read_csv(file=paste(raw_dir, '3.1_DPSS/D3.5.BMat2019_datainputs.csv', sep="/")) %>%
filter(modelinclude=='TRUE') %>% #keep just observations that the UN inter-agency group deemed credible for use in modelling
mutate(date=end,
iso3c=iso,
D3.MMRT=pm_obs)
#Now loop from 2016 and 2019, keeping just data inside last 5 years.
for (reference_year in 2016:2019) {
temp <-D3.5.AKI.MMR %>%
mutate(frequency=((reference_year-as.numeric(date))<=5) & (reference_year>=as.numeric(date))) %>%
mutate(SPI.FREQ.D3.MMRT=if_else(frequency==TRUE,1,0)) %>% #create 0,1 variable for whether data point exists for country
mutate(SPI.QUAL.D3.MMRT=case_when(
reference_year-as.numeric(date)>=5 ~ 0, #outside 5 year window
type=='vr' ~ 0.5,
type!='vr' ~ 0.25,
TRUE ~ as.numeric(NA)
)) %>%
group_by(iso3c) %>%
summarise(SPI.FREQ.D3.MMRT=sum(SPI.FREQ.D3.MMRT, na.rm=T),
SPI.QUAL.D3.MMRT=max(SPI.QUAL.D3.MMRT, na.rm=T)) %>%
mutate(SPI.FREQ.D3.MMRT=case_when(
SPI.FREQ.D3.MMRT>=3 ~ 0.5,
SPI.FREQ.D3.MMRT==2 ~ 0.3,
SPI.FREQ.D3.MMRT==1 ~ 0.15,
SPI.FREQ.D3.MMRT==0 ~ 0,
TRUE ~ 0
)) %>%
mutate(SPI.D3.MMRT=SPI.FREQ.D3.MMRT+SPI.QUAL.D3.MMRT) %>%
mutate(date=reference_year) %>%
select( iso3c, date, starts_with('SPI.'))
assign(paste("D3.5.AKI",reference_year,sep="_"), temp)
}
D3.5.AKI <- bind_rows(D3.5.AKI_2016, D3.5.AKI_2017, D3.5.AKI_2018, D3.5.AKI_2019)AKI 3.7: Access to electricity (% of population)
Access to Electricity (% of population) has 99% coverage in the WDI database making it less useful for distinguishing between countries. In order to improve the usefulness of this indicator, we are making use of data compiled by World Bank colleagues on surveys containing electricity items. According to the access to electricity metadata, around 42 countries have no data and an imputed value is assigned based on the regional average. By going to survey source data, we address this issue of imputation, which gives a misleading picture of availability for our purposes. Below is the metadata from the WDI for methods for the access to electricity indicator.
Data for access to electricity are collected among different sources: mostly data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Given the low frequency and the regional distribution of some surveys, a number of countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modeling approach was adopted to fill in the missing data points - around 1990, around 2000, and around 2010. Therefore, a country can have a continuum of zero to three data points. There are 42 countries with zero data point and the weighted regional average was used as an estimate for electrification in each of the data periods. 170 countries have between one and three data points and missing data are estimated by using a model with region, country, and time variables. The model keeps the original observation if data is available for any of the time periods. This modeling approach allowed the estimation of electrification rates for 212 countries over these three time periods (Indicated as “Estimate”). Notation “Assumption” refers to the assumption of universal access in countries classified as developed by the United Nations. Data begins from the year in which the first survey data is available for each country.
Scoring 1 Point. 3 or more values available within past 5 years 0.6 Points. 2 values available within past 5 years;
0.3 Points. 1 values available within past 5 years;
0 Points. None within past 5 years
#read in file from rawdata folder
D3.7.ELEC <- read_csv(file = paste(raw_dir, "3.2_DPES/D3.7.ELEC.csv", sep="/" )) %>%
rename(country=Country,
iso3c="Country code",
date=Time)
#Now loop from 2016 and 2019, keeping just data inside last 5 years.
for (reference_year in 2016:2019) {
temp <-D3.7.ELEC %>%
mutate(frequency=((reference_year-as.numeric(date))<=5) & (reference_year>=as.numeric(date))) %>%
mutate(SPI.D3.ELEC=if_else(frequency==TRUE,1,0)) %>% #create 0,1 variable for whether data point exists for country
group_by(country) %>%
summarise(SPI.D3.ELEC=sum(SPI.D3.ELEC, na.rm=T)) %>%
mutate(SPI.D3.ELEC=case_when(
SPI.D3.ELEC>=3 ~ 1,
SPI.D3.ELEC==2 ~ 0.6,
SPI.D3.ELEC==1 ~ 0.3,
SPI.D3.ELEC==0 ~ 0,
TRUE ~ 0
)) %>%
mutate(date=reference_year) %>%
select( country, date, starts_with('SPI.'))
assign(paste("D3.7.AKI",reference_year,sep="_"), temp)
}
D3.7.AKI <- bind_rows(D3.7.AKI_2016, D3.7.AKI_2017, D3.7.AKI_2018, D3.7.AKI_2019)AKI 3.14 Quarterly GDP
Quarterly GDP numbers were pulled from the IMF website.
After GDP by expenditure, quarterly GDP is probably the most important development to be made in a system of National Accounts, before the development of full sectoral accounts. IMF IFS has quarterly GDP from various sources, including governments and international agencies at https://data.imf.org/?sk=4C514D48-B6BA-49ED-8AB9-52B0C1A0179B
Scoring 1 Point. 3 or more values available within past 5 years 0.6 Points. 2 values available within past 5 years;
0.3 Points. 1 values available within past 5 years;
0 Points. None within past 5 years
#read in file from rawdata folder
D3.14.QUART.GDP <- read_csv(file = paste(raw_dir, "3.2_DPES/D3.14.QUART.GDP.csv", sep="/" ))
#clean data and produce indicator for each year
#Now loop from 2016 and 2019, keeping just data inside last 5 years.
for (reference_year in 2016:2019) {
temp <-D3.14.QUART.GDP %>%
mutate(date=as.numeric(str_sub(TIME_PERIOD,1,4))) %>%
mutate(iso2c=REF_AREA) %>%
mutate(frequency=((reference_year-as.numeric(date))<=5) & (reference_year>=as.numeric(date))) %>%
mutate(SPI.D3.QUART.GDP=if_else(frequency==TRUE,1,0)) %>% #create 0,1 variable for whether data point exists for country
group_by(iso2c, date) %>%
summarise(SPI.D3.QUART.GDP=max(SPI.D3.QUART.GDP, na.rm=T)) %>%
group_by(iso2c) %>%
summarise(SPI.D3.QUART.GDP=sum(SPI.D3.QUART.GDP, na.rm=T)) %>%
mutate(SPI.D3.QUART.GDP=case_when(
SPI.D3.QUART.GDP>=3 ~ 1,
SPI.D3.QUART.GDP==2 ~ 0.6,
SPI.D3.QUART.GDP==1 ~ 0.3,
SPI.D3.QUART.GDP==0 ~ 0,
TRUE ~ 0
)) %>%
mutate(date=reference_year) %>%
select( iso2c, date, starts_with('SPI.D3.QUART.GDP'))
assign(paste("D3.14.AKI",reference_year,sep="_"), temp)
}
D3.17.AKI <- bind_rows(D3.14.AKI_2016, D3.14.AKI_2017, D3.14.AKI_2018, D3.14.AKI_2019) AKI 3.15 Debt service (PPG and IMF only, % of exports of goods, services and primary income)
For this indicator, Debt service (PPG and IMF only, % of exports of goods, services and primary income), we will pull data from the WDI but modify the scoring using the WDI metadata on whether the external debt data is actual, estimated, or preliminary. The status “as reported (actual)” indicates that the country was fully current in its reporting under the DRS and that World Bank staff are satisfied that the reported data give an adequate and fair representation of the country’s total public debt. “Preliminary” data are based on reported or collected information, but because of incompleteness or other reasons, an element of staff estimation is included. “Estimated” data indicate that countries are not current in their reporting and that a significant element of staff estimation has been necessary for producing the data tables.
Scoring is as follows:
Frequency:
0.5 Point. 3 or more values available within past 5 years
0.3 Points. 2 values available within past 5 years;
0.15 Points. 1 values available within past 5 years;
0 Points. None within past 5 years
Quality:
0.5 Points. Actual value 0.3 Points. Preliminary value 0.15 Points. Estimated value 0 Points. No value
#reshape metaadata file
metadata_3.15 <- WDI_metadata %>%
transmute(iso3c=if_else(is.na(Country.Code), Code, Country.Code),
date=date,
External_debt_Reporting=External.debt.Reporting.status) %>%
mutate(SPI.QUAL.D3.DT.TDS.DPPF.XP.ZS= case_when(
External_debt_Reporting=='Actual' ~ 0.5,
External_debt_Reporting=='Preliminary' ~ 0.3,
External_debt_Reporting=='Estimate' ~ 0.15,
TRUE ~ 0
))
#pull data from wdi and merge with metadata
for (reference_year in 2016:2019) {
temp <-wbstats::wb(country="countries_only",
indicator='DT.TDS.DPPF.XP.ZS',
startdate=reference_year-5,
enddate=reference_year,
return_wide = T,
removeNA=FALSE) %>%
filter(((reference_year-as.numeric(date))<=5) & (reference_year>=as.numeric(date))) %>% #filter out years outside reference window of 3 years
mutate_at(.vars=c('DT.TDS.DPPF.XP.ZS'), ~if_else(is.na(.),0,1)) %>% #create 0,1 variable for whether data point exists for country
group_by(iso3c, country) %>%
summarise_all((~(if(is.numeric(.)) sum(., na.rm = TRUE) else first(.)))) %>% #group by country to create one observation per country containing whether or not data point existed
mutate(SPI.FREQ.D3.DT.TDS.DPPF.XP.ZS = case_when(
DT.TDS.DPPF.XP.ZS>=3 ~ 0.5,
DT.TDS.DPPF.XP.ZS==2 ~ 0.3,
DT.TDS.DPPF.XP.ZS==1 ~ 0.15,
DT.TDS.DPPF.XP.ZS==0 ~ 0,
TRUE ~ 0
)) %>% # 0.5 point for at least 3 values, 0.3 for 2 values, 0.15 for 1 values, 0 otherwise
mutate(date=reference_year) %>%
left_join(metadata_3.15) %>% #attach country metadata
mutate(SPI.QUAL.D3.DT.TDS.DPPF.XP.ZS= case_when(
External_debt_Reporting=='Actual' ~ 0.5,
External_debt_Reporting=='Preliminary' ~ 0.3,
External_debt_Reporting=='Estimate' ~ 0.15,
TRUE ~ 0
)) %>% # 0.5 point for actual, 0.3 for preliminary, 0.15 for estimate
mutate(
SPI.D3.DT.TDS.DPPF.XP.ZS=(SPI.QUAL.D3.DT.TDS.DPPF.XP.ZS + SPI.FREQ.D3.DT.TDS.DPPF.XP.ZS)) %>%
ungroup() %>%
select(iso3c, country, date, contains('D3.DT.TDS.DPPF.XP.ZS'))
assign(paste("D3.15.AKI",reference_year,sep="_"), temp)
}
D3.15.AKI <- bind_rows(D3.15.AKI_2016, D3.15.AKI_2017, D3.15.AKI_2018, D3.15.AKI_2019) #now combine AKI databases and write to csv
D3.AKI <- bind_rows(D3.AKI_2016, D3.AKI_2017, D3.AKI_2018, D3.AKI_2019) %>%
left_join(D3.1.AKI) %>%
left_join(D3.2.AKI) %>%
left_join(D3.3.AKI) %>%
left_join(D3.5.AKI) %>%
left_join(D3.7.AKI) %>%
left_join(D3.15.AKI) %>%
left_join(D3.17.AKI) %>%
mutate_at(vars(starts_with('SPI.D3.')), ~if_else(is.na(.),0,as.numeric(.)))
spi_df <- spi_df %>%
left_join(D3.AKI)aki <- c(
SPI.D3.POV='Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)',
SPI.D3.FIES='Food Insecurity Experience Scale',
SPI.D3.CHLD.MORT='Mortality rate, under-5 (per 1,000 live births)',
SPI.D3.SE.LPV.PRIM.BMP='Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold',
SPI.D3.MMRT='Maternal Mortality',
SPI.D3.SH.H2O.SMDW.ZS='People using safely managed drinking water services (% of population)',
SPI.D3.ELEC='Access to electricity (% of population)',
SPI.D3.SL.UEM.TOTL.NE.ZS='Unemployment, total (% of total labor force) (national estimate)' ,
SPI.D3.NV.IND.MANF.ZS='Manufacturing, value added (% of GDP)',
SPI.D3.SI.SPR.PC40.ZG='Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)',
SPI.D3.ER.H2O.FWST.ZS='Level of water stress: freshwater withdrawal as a proportion of available freshwater resources',
SPI.D3.EG.FEC.RNEW.ZS='Renewable energy consumption (% of total final energy consumption)',
SPI.D3.NE.CON.PRVT.CN='Households and NPISHs Final consumption expenditure (current LCU)',
SPI.D3.QUART.GDP='Quarterly GDP',
SPI.D3.DT.TDS.DPPF.XP.ZS='Debt service (PPG and IMF only, % of exports of goods, services and primary income)'
)
for (ind in names(aki)) {
ind <- ind
names(aki)
print(ind)
temp_map <- D3.AKI %>%
filter(.data[[ind]] > 0)
spi_mapper('temp_map', ind, aki[[ind]])
}## [1] "SPI.D3.POV"
## [1] "SPI.D3.FIES"
## [1] "SPI.D3.CHLD.MORT"
## [1] "SPI.D3.SE.LPV.PRIM.BMP"
## [1] "SPI.D3.MMRT"
## [1] "SPI.D3.SH.H2O.SMDW.ZS"
## [1] "SPI.D3.ELEC"
## [1] "SPI.D3.SL.UEM.TOTL.NE.ZS"
## [1] "SPI.D3.NV.IND.MANF.ZS"
## [1] "SPI.D3.SI.SPR.PC40.ZG"
## [1] "SPI.D3.ER.H2O.FWST.ZS"
## [1] "SPI.D3.EG.FEC.RNEW.ZS"
## [1] "SPI.D3.NE.CON.PRVT.CN"
## [1] "SPI.D3.QUART.GDP"
## [1] "SPI.D3.DT.TDS.DPPF.XP.ZS"
Data Sources
Cleaning for the Data Sources Indicators. Data Sources (4 Indicators):
- 4.1_SOCS - Indicator 4.1: censuses and surveys
- 4.2_SOAD - Indicator 4.2: administrative data
- 4.3_SOGS - Indicator 4.3: geospatial data
- 4.4_SOPC - Indicator 4.4: private/citizen generated data
Indicator 4.1: censuses and surveys
This indicator draws from data collected by the Statistical Performance Indicators team. The following censuses and surveys are considered:
- Population & Housing census
- Agriculture census
- Business/establishment census
- Household Survey on income/ consumption/ expenditure/ budget/ Integrated Survey
- Agriculture survey
- Labor Force Survey
- Health/Demographic survey
- Business/establishment survey
We will just show the map and data for the population census, but others maps can be generated and this has been collected already by SPI team.
cs_fun <- function(data, input_var) {
data <- data
input_var <- input_var
#read in csv file.
cs_df <- read_csv(file = paste(raw_dir, '/4.1_SOCS/', data, ".csv", sep="" )) %>%
group_by(iso3c, country) %>%
rename(input_var = !! input_var) %>%
nest() %>% # The next chunk of code will split our string with the years of the census (i.e. "2000, 2010") in to separate rows. We will then aggregate up.
mutate(
temp_col = map(
data,
~ str_extract_all(.x$input_var, "\\d{4}") %>%
flatten() %>%
map_chr(~return(.x)) %>%
as_tibble()
)
) %>%
unnest(keep_empty = TRUE) %>% # Now we have a database with the observations equal to Country*Census observations. From here we can calculate latest census, etc.
mutate(indicator_date=as.numeric(value))
#Now calculate our SPI score for this indicator
for (i in 2004:2019) {
temp <- cs_df %>%
mutate(date=i) %>%
mutate(recency_indicator=((date>indicator_date)) ) %>% #restrict to censuses that do not occur after reference date
mutate(indicator_date=if_else(recency_indicator==TRUE, indicator_date, as.numeric(NA))) %>%
group_by(iso3c, country, date, database_last_updated, input_var ) %>%
summarise(last_val=max(indicator_date, na.rm=T)) %>%
mutate(indicator=case_when(
(date-last_val<=10) & (date-last_val>0) ~ 1,
(date-last_val<=20) & (date-last_val>0) ~ 0.5,
TRUE ~ 0 )
) %>%
ungroup() %>%
select(c('iso3c', 'country', 'date', 'input_var', 'indicator') ) %>%
arrange(date, country)
assign(paste('temp',i,sep="_"), temp)
}
temp <- temp_2019
for (i in 2004:2018) {
temp <- bind_rows(temp, get(paste('temp',i,sep="_")))
}
temp
}
#Population Censuses
cs1_df <-cs_fun('D4.1.1.CEN.POPU', 'POPU.CENSUS') %>%
rename(POPU.CENSUS=input_var,
SPI.D4.1.1.POPU=indicator)
#Agriculture census
cs2_df <-cs_fun('D4.1.2.CEN.AGRI', 'AGRI.CENSUS') %>%
rename(AGRI.CENSUS=input_var,
SPI.D4.1.2.AGRI=indicator)
#Business/establishment census
cs3_df <-cs_fun('D4.1.3.CEN.BIZZ', 'BIZZ.CENSUS') %>%
rename(BIZZ.CENSUS=input_var,
SPI.D4.1.3.BIZZ=indicator)
#Household Survey on income/ consumption/ expenditure/ budget/ Integrated Survey
cs4_df <-cs_fun('D4.1.4.SVY.HOUS', 'HOUS.SURVEYS') %>%
rename(HOUS.SURVEYS=input_var,
SPI.D4.1.4.HOUS=indicator)
#Agriculture survey
cs5_df <-cs_fun('D4.1.5.SVY.AGRI', 'AGRI.SURVEYS') %>%
rename(AGRI.SURVEYS=input_var,
SPI.D4.1.5.AGSVY=indicator)
#Labor Force Survey
cs6_df <-cs_fun('D4.1.6.SVY.LABR', 'LABR.SURVEYS') %>%
rename(LABR.SURVEYS=input_var,
SPI.D4.1.6.LABR=indicator)
#Health/Demographic survey
cs7_df <-cs_fun('D4.1.7.SVY.HLTH', 'HLTH.SURVEYS') %>%
rename(HLTH.SURVEYS=input_var,
SPI.D4.1.7.HLTH=indicator)
#Business/establishment survey
cs8_df <-cs_fun('D4.1.8.SVY.BIZZ', 'BIZZ.SURVEYS') %>%
rename(BIZZ.SURVEYS=input_var,
SPI.D4.1.8.BZSVY=indicator)
#brind all censuses and surveys together
cs_df <- cs1_df %>%
left_join(cs2_df) %>%
left_join(cs3_df) %>%
left_join(cs4_df) %>%
left_join(cs5_df) %>%
left_join(cs6_df) %>%
left_join(cs7_df) %>%
left_join(cs8_df)
#add to spi databases
spi_df <- spi_df %>%
left_join(cs_df)
#now do the figures for population censuses
cs_df_map <- cs_df %>%
filter(!is.na(POPU.CENSUS))
spi_mapper('cs_df_map', 'POPU.CENSUS', 'Population Census Available in Past 20 Years in 2019' )
Indicator 4.2: administrative data
The following indicator checks whether administrative data is available for the following topic areas: Social Protection, Education, Population & Health, and Labor
Education Admin Data
- UNESCO UIS has shared a database containing data we can use on administrative data systems
Two areas:
Data tracking SDG 4.1.2 Administration of a nationally-representative learning assessment (a) in Grade 2 or 3; (b) at the end of primary education; and (c) at the end of lower secondary education
Data tracking whether countries produce the following from administrative data sources:
- Out-of-school rate by school age and sex
- 4.2.2 Participation rate in organized learning (one year before the official primary entry age), by sex
- Out-of-school rate by school age and sex
#read data
unesco_df_raw <- read_csv(paste(raw_dir, '4.2_SOAD','unesco_admin_sources.csv', sep="/"))
#cleaning of aspire data
unesco_df <- unesco_df_raw %>%
mutate(date=TIME) %>%
group_by( LOCATION2017, date) %>%
summarise(Value=mean(Value, na.rm=T)) %>%
rename(iso3c=LOCATION2017) %>%
left_join(country_metadata) %>%
mutate(available=(!is.na(Value))
) %>%
filter(available==TRUE)
spi_mapper('unesco_df', 'available', 'Education Administrative Data Available According to UIS' )
Population & Health Admin Data
- This indicator is formed using World Bank metadata on whether the Civil Registration and Vital Statistics (CRVS) system is complete in the country.
Civil registration is the act of recording and documenting of vital events in a person’s life (including birth, marriage, divorce, adoption, and death and cause of death) and is a fundamental function of national governments. Birth registration establishes an individual’s legal identity at birth. A legal identity, name, nationality, and proof of age, are important human rights. They enable individuals to be included in various government, social and private services, and include the right to vote, etc. Vital statistics are compiled using civil registration information on these vital events. The availability of reliable and up-to-date vital statistics depends on the level of development of civil registration programs. An effective civil registration and vital statistics (CRVS) system is critical for planning and monitoring programs across several sectors.
By complete, that is representing 90 per cent or more of the events occurring in the specified year.
Source: World Bank WDI Metadata.
Request_metadata <- GET(url = "http://api.worldbank.org/v2/country/all/indicator/3.11.01.03.popreg?format=json&date=2004:2019&per_page=5000")
Response_metadata <- content(Request_metadata, as = "text", encoding = "UTF-8")
# Parse the JSON content and convert it to a data frame.
crvs_df <- jsonlite::fromJSON(Response_metadata, flatten = TRUE) %>%
data.frame() %>%
transmute(
iso3c=countryiso3code,
country=country.value,
date=as.numeric(date),
crvs=value
) %>%
left_join(spi_df_empty) #add on country metadata
# Manipulate and clean final data
crvs_df <- crvs_df %>%
filter(!is.na(income)) #keep just countries (drop aggregations)
crvs_map <- crvs_df %>%
filter(!is.na(crvs))
spi_mapper('crvs_map', 'crvs', 'Complete Civil Registration & Vital Statistics Data Available According to WDI' )
Labor Admin Data
- ILO shared database containing all administrative data sources used to produce ILO statistics (ILOSTAT)
- May not be comprehensive of all administrative datasets available in a country
- But gives some indication of whether labor statistics produced using Admin data
- May not be comprehensive of all administrative datasets available in a country
Database contains the following admin sources:
- Insurance records
- Labour inspectorate records
- Records of employers’ organizations
- Employment office records
- Records of workers’ organizations
- Other administrative records and related sources
- Insurance records
Database extends from 2010 to 2019 and covers 177 countries
#read data
ilo_df_raw <- read_csv(paste(raw_dir, '4.2_SOAD','ILOSTAT_sources.csv', sep="/"))
#cleaning of aspire data
ilo_df <- ilo_df_raw %>%
group_by(iso3c, date) %>%
summarise(Source.type=last(Source.type)) %>%
left_join(country_metadata)
spi_mapper('ilo_df', 'Source.type', 'Labor Administrative Data Available According to ILO' )
Indicator 4.3: geospatial data
New indicator based on references to geospatial data in metadata relating to content on NSO website
Source
Our source for this indicator is Open Data Watch. From Open Data watch:
The Open Data Inventory (ODIN) assesses the coverage and openness of official statistics to help identify gaps, promote open data policies, improve access, and encourage dialogue between national statistical offices (NSOs) and data users. ODIN 2018/19 includes 178 countries, including most all OECD countries. Two-year comparisons are for all countries with two years of data between 2015-2017. Scores can be compared across topics and countries.
We use their indicator on whether indicators are available at the first or second administrative level. To identify the first administrative levels, ODIN largely draws on the ISO 3166-2 standard. In many countries, first administrative levels refer to governorates, regions, or province. No official list exists for the second administrative level classifications. If geographical disaggregation exists that does not qualify as first administrative level, assume that the data are disaggregated to the second administrative level as long as the classification appears to be a further divisions of the first administrative level.
Scoring for the ODIN indicators for geospatial information is below:
- 1 point if all published data in a data category are available at first/second administrative level.
- 0.5 points if some published data in a data category are available at first/second administrative level.
- 0 points if no data are available at this level
There are 21 data categories.
Social Statistics
1. Population and Vital Statistics
2. Education Facilities
3. Education Outcomes
4. Health Facilities
5. Health Outcomes
6. Reproductive Health
7. Gender Statistics
8. Crime and Justice Statistics
9. Poverty Statistics
Economic Statistics
10. National Accounts
11. Labor Statistics
12. Price Indexes
13. Government Finance
14. Money and Banking
15. International Trade
16. Balance of Payments
Environmental Statistics
17. Land Use
18. Resource Use
19. Energy Use
20. Pollution
21. Built Environment
For the first administrative unit: Money & Banking, International Trade, and Balance of Payments are not scored for this element. For various indicators, lenient interpretations are used for first administrative divisions.
For the second administrative unit: Money & Banking, International Trade, Balance of Payments, National Accounts, Government Finance ,Pollution, Energy Use, Price Indexes, and Resource Use are not scored for this element. For various indicators within categories, second administrative level data is not required as well.
In the scores we present below, we show a score between 0 and 1 with a maximum score of 1, which would mean the country has geo data in full for 100% of elements. A score of 0 indicates no data at all for any elements.
More details on the geographic disaggregation considerations can be found in their technical manual:
https://docs.google.com/document/d/1ubPL1l_3im9bjlCVZ6W2ICAy6UAiXl1hGeA1aXImkxI/edit
#read in ODIN data
for (i in 2015:2018) {
temp <- read_csv(paste(raw_dir, '/2.2_DSOA/','ODIN_',i,'.csv', sep="")) %>%
as_tibble(.name_repair = 'universal') %>%
mutate(date=i) %>%
filter(Data.categories=='All Categories')
assign(paste('geo_df',i,sep="_"), temp)
}
#bind different years together
geo_df <- bind_rows(geo_df_2015, geo_df_2016, geo_df_2017, geo_df_2018)
#create geo scores
geo_df <- geo_df %>%
select(Country.Code, date, First.administrative.level, Second.administrative.level) %>%
rename(iso3c=Country.Code) %>%
group_by( iso3c) %>%
right_join(country_metadata) %>%
mutate(First.administrative.level=if_else(is.na(First.administrative.level), 0, First.administrative.level),
Second.administrative.level=if_else(is.na(Second.administrative.level), 0, Second.administrative.level))
#add to spi df
spi_df <- spi_df %>%
left_join(geo_df)
Indicator 4.4: private/citizen generated data
New indicator based on references to private/citizen generated data in metadata relating to content on NSO website.
Covid Mobility Data Availability
We have seen an increased use of private/citizen generated data during the COVID-19 pandemic. One way in which it has been used is for tracking mobility of citizens to understand the social distancing measures citizens are taking. As a measure of private/citizen generated data we make use of Apple and Google Mobility tracking data made publicly available during the pandemic.
Source:
Google LLC “Google COVID-19 Community Mobility Reports”. https://www.google.com/covid19/mobility/ Accessed: 2020-07-23.
Apple “Mobility Trends Reports”. https://www.apple.com/covid19/mobility/ Accessed: 2020-07-23.
#####
# Google
#####
#google mobility
google_df_raw <- read_csv(paste(raw_dir, '4.4_SOPC','Global_Mobility_Report.csv', sep="/"))
#match to world bank country database
google_df <- google_df_raw %>%
group_by(country_region_code) %>%
summarise(freq=n()) %>%
left_join(country_metadata, by=c('country_region_code'='iso2c')) %>%
mutate(date=2020)
spi_mapper('google_df', 'date', 'Google Global Mobility Report Data' )
#####
# Apple
#####
apple_df_raw <- read_csv(paste(raw_dir, '4.4_SOPC','applemobilitytrends-2020-07-26.csv', sep="/"))
#match to world bank country database
apple_df <- apple_df_raw %>%
select(-c('country', 'alternative_name', 'sub-region')) %>%
filter(geo_type=="country/region") %>%
rename(country=region) %>%
pivot_longer(cols=starts_with("2020"),
names_to='date',
values_to='value') %>%
group_by(country) %>%
summarise(across(everything(), last)) %>%
mutate(country=case_when(
country=="Egypt" ~ "Egypt, Arab Rep.",
country=="Republic of Korea" ~ "Korea, Rep.",
country=="Macao" ~ "Macao SAR, China",
country=="Slovakia" ~ "Slovak Republic",
country=="Taiwan" ~ "Taiwan, China",
TRUE ~ country
)) %>%
group_by(country) %>%
summarise(freq=n()) %>%
left_join(country_metadata) %>%
mutate(date=2020)
spi_mapper('apple_df', 'date', 'Apple Mobility Report Data' )
Data Infrastructure
Data Infrastructure (5 Indicators):
- 5.1_DILG - Indicator 5.1: legislation and governance
- 5.2_DISM - Indicator 5.2: standards
- 5.3_DISK - Indicator 5.3: skills
- 5.4_DIPN - Indicator 5.4: partnerships
- 5.5_DIFI - Indicator 5.5: finance
Social Protection Admin Data